We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.
The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.
We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).
By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.
S.V.T. and M.S.G. are supported in part by R01 CA164944 (mismatch repair variants). S.V.T. and K.M.B. are supported in part by P30 CA042014 (Cancer Center Support grant). S.V.T. is supported in part by R01 CA121245 (BRCA gene variants). M.S.G. is supported in part by U01 HG007437 (Clinical Genome Resource). S.M.H. is supported in part by U41 HG006834 (Clinical Genome Resource). S.A.P. is supported in part by U01 HG007436 (Clinical Genome Resource). L.G.B. is supported in part by ZIA HG200387 03 and ZIA HG200388 03 (Intramural Research Program of the National Human Genome Research Institute).
The members of the Clinical Genome Resource, Sequence Variant Interpretation Working Group
Antonis Antoniou, Cambridge University, Cambridge, UK; Jonathan S. Berg, University of North Carolina, Chapel Hill, NC; Leslie G. Biesecker, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; co-chair; Steven E. Brenner, University of California, Berkeley, Berkeley, CA; Fergus Couch, Mayo Clinic, Rochester, MN; Garry Cutting, Department of Human Genetics, Johns Hopkins University School of Medicine, Baltimore, MD; Marc S. Greenblatt, University of Vermont; Robert Larner, College of Medicine, Burlington, VT; Steven M. Harrison, Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, MA; co-chair; Christopher D. Heinen, University of Connecticut Health, Farmington, CT; Matthew E. Hurles, Wellcome Trust Sanger Institute, Hinxton, UK; H. Peter Kang, Counsyl, San Francisco, CA; Rachel Karchin, Johns Hopkins University School of Medicine, Baltimore, MD; Robert L. Nussbaum, Invitae, San Francisco, CA; Sharon E. Plon, Baylor College of Medicine, Houston, TX; Heidi L. Rehm, Partners HealthCare Laboratory for Molecular Medicine and Harvard Medical School, Boston, MA; Sean V. Tavtigian, Department of Oncological Science and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT.
Supplementary material is linked to the online version of the paper at http://www.nature.com/gim